scArches – mapping single-cell data to reference atlases by transfer learning

Large single-cell atlases are now routinely generated to serve as references for analysis of smaller-scale studies. Yet learning from reference data is complicated by batch effects between datasets, limited availability of computational resources and sharing restrictions on raw data. Here researchers from the Helmholtz Center Munich  introduce a deep learning strategy for mapping query datasets on top of a reference called single-cell architectural surgery (scArches). scArches uses transfer learning and parameter optimization to enable efficient, decentralized, iterative reference building and contextualization of new datasets with existing references without sharing raw data. Using examples from mouse brain, pancreas, immune and whole-organism atlases, the researchers show that scArches preserves biological state information while removing batch effects, despite using four orders of magnitude fewer parameters than de novo integration. scArches generalizes to multimodal reference mapping, allowing imputation of missing modalities. Finally, scArches retains coronavirus disease 2019 (COVID-19) disease variation when mapping to a healthy reference, enabling the discovery of disease-specific cell states. scArches will facilitate collaborative projects by enabling iterative construction, updating, sharing and efficient use of reference atlases.

scArches enables iterative query-to-reference single-cell integration

Fig. 1

a, Pre-training of a latent representation using public reference datasets and corresponding reference labels. b, Decentralized model building: users download parameters for the atlas of interest, fine tune the model and optionally upload their updated model for other users. ce, Illustration of this workflow for a human pancreas atlas across different scArches base models. Training a reference atlas across three human pancreas datasets (CelSeq, InDrop, Fluidigm C1), uniform manifold approximation and projection (UMAP) embedding for the original (c) and the integrated reference for pre-trained reference models (d,e, first column). Second column in d,e, querying a new SS2 dataset to the integrated reference. Updating the cell atlas with a fifth dataset (CelSeq2). Third column in d,e, black dashed circles represent cells absent in the reference data. UMAP plots are based on the model embedding.

Availability – Software is available at

Lotfollahi M, Naghipourfar M, Luecken MD et al. (2021) Mapping single-cell data to reference atlases by transfer learning. Nat Biotechnol [Epub ahead of print]. [article]

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